Triple
T7762644
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Infanta of Spain |
E176063
|
entity |
| Predicate | equivalentTitleMale |
P15994
|
FINISHED |
| Object | Infante of Spain |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Infante of Spain | Statement: [Infanta of Spain, equivalentTitleMale, Infante of Spain]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: equivalentTitleMale Context triple: [Infanta of Spain, equivalentTitleMale, Infante of Spain]
-
A.
maleEquivalent
chosen
Indicates that one entity is the corresponding male counterpart or equivalent of another entity.
-
B.
equivalentOrRelatedTitle
Indicates that two titles are the same or sufficiently similar in meaning, role, or status to be treated as equivalent or closely related.
-
C.
equivalentTitleInJapanese
Indicates that one entity has a corresponding or matching title in Japanese that is equivalent in meaning or usage to the other entity’s title.
-
D.
officeHolderTitleWhenMale
Indicates the specific title used for a person holding an office when that office holder is male.
-
E.
equivalentTitleInPortuguese
Indicates that one entity has a title that is the equivalent of another entity’s title, specifically in Portuguese.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69c69962923c8190ac74d28b4f9fe0a0 |
completed | March 27, 2026, 2:51 p.m. |
| NER | Named-entity recognition | batch_69c705257ca08190a78c592a1e616da8 |
completed | March 27, 2026, 10:31 p.m. |
| PD | Predicate disambiguation | batch_69c7016df2b08190b2330a2010691431 |
completed | March 27, 2026, 10:15 p.m. |
Created at: March 27, 2026, 4:09 p.m.